Heart disease is the leading cause of death in the United States. A heart attack, also known as an acute myocardial infarction (AMI), typically results from a blood clot or “thrombus” that obstructs blood flow in one or more coronary arteries. AMI is a common and life-threatening complication of coronary artery disease. Coronary ischemia is caused by an insufficiency of oxygen to the heart muscle. Ischemia is typically provoked by physical activity or other causes of increased heart rate when one or more of the coronary arteries is narrowed by atherosclerosis. AMI, which is typically the result of a completely blocked coronary artery, is the most extreme form of ischemia. Patients will often (but not always) become aware of chest discomfort, known as “angina”, when the heart muscle is experiencing ischemia. Those with coronary atherosclerosis are at higher risk for AMI if the plaque becomes further obstructed by thrombus.
Detection of AMI often involves analyzing changes in a person's ST segment voltage. A common scheme for computing changes in the ST segment involves determining a quantity known as ST deviation for each beat. ST deviation is the value of the electrocardiogram at a point or points during the ST segment relative to the value of the electrocardiogram at some point or points during the PQ segment. Whether or not a particular ST deviation is indicative of AMI depends on a comparison of that ST deviation with a threshold.
Quantifying the extent of ST (or other parameter) changes is often not simple. U.S. Pat. No. 6,217,525 to Medema et al. employ a Mahalanobis distance to quantify the difference between a parameter value (being tested) and mean parameter values derived from ischemic and non-ischemic populations, respectively. A Mahalanobis distance involves the normalization of a difference of two values of a random variable by the standard deviation of that value. Jager et. al. (Detection of Transient ST Segment Episodes During Ambulatory ECG Monitorign, Computers and Biomedical Research 31, 305-322 (1998)) employ a patient/lead specific Mahalanobis distance measure to determine the difference between a parameter value (being tested) and reference parameter value that characterized past values of the parameter. The parameters described by Jager et al. are ST segment Karhunen-Loèeve feature vectors. Pueyo et al. (High-Frequency Signature of the QRS Complex across ischemia Quantified by QRS Slopes, Computers in Cardiology 2005; 32:659-662) quantify the ischemia induced change of a parameter (including ST deviation) by dividing a raw measure of the inflation induced change by the standard deviation of the parameters (observed during a control period before the inflation).
In multiple lead systems, ST changes must be both quantified and combined in some fashion. A commonly used clinical approach for combining ST deviations from different leads involves summing the absolute values of ST deviations from different leads and comparing the result to a threshold. Multiple lead/parameter statistically based approaches have been described. Despite these prior schemes, there is still a need for an effective method for combining information from different leads.